Predict() - Maybe I'm not understanding it

First, you want to use

model <- lm(Total ~ Coupon, data=df)

not model <-lm(df$Total ~ df$Coupon, data=df).

Second, by saying lm(Total ~ Coupon), you are fitting a model that uses Total as the response variable, with Coupon as the predictor. That is, your model is of the form Total = a + b*Coupon, with a and b the coefficients to be estimated. Note that the response goes on the left side of the ~, and the predictor(s) on the right.

Because of this, when you ask R to give you predicted values for the model, you have to provide a set of new predictor values, ie new values of Coupon, not Total.

Third, judging by your specification of newdata, it looks like you're actually after a model to fit Coupon as a function of Total, not the other way around. To do this:

model <- lm(Coupon ~ Total, data=df)
new.df <- data.frame(Total=c(79037022, 83100656, 104299800))
predict(model, new.df)

Thanks Hong, that was exactly the problem I was running into. The error you get suggests that the number of rows is wrong, but the problem is actually that the model has been trained using a command that ends up with the wrong names for parameters.

This is really a critical detail that is entirely non-obvious for lm and so on. Some of the tutorial make reference to doing lines like lm(olive$Area@olive$Palmitic) - ending up with variable names of olive$Area NOT Area, so creating an entry using anewdata<-data.frame(Palmitic=2) can't then be used. If you use lm(Area@Palmitic,data=olive) then the variable names are right and prediction works.

The real problem is that the error message does not indicate the problem at all:

Warning message: 'anewdata' had 1 rows but variable(s) found to have X rows